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AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory
Addressing the communication bottleneck inherent in federated learning (FL), over-the-air FL (AirFL) has emerged as a promising solution, which is, however, hampered by deep fading conditions.
Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI
With the advent of emerging IoT applications such as autonomous driving, digital-twin and metaverse etc.
Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clipping
For additive white Gaussian noise (AWGN) channels with instantaneous per-device power constraints, prior work has demonstrated the optimality of a power control mechanism based on norm clipping.
Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis
The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over siloed data centers is motivating renewed interest in the collaborative training of a shared model by multiple individual clients via federated learning (FL).
Decentralized Federated Learning via SGD over Wireless D2D Networks
In this paper, wireless protocols are proposed that implement DSGD by accounting for the presence of path loss, fading, blockages, and mutual interference.
Information Theory Networking and Internet Architecture Signal Processing Information Theory
